Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use for Electricity Maps contrib parser development, capacity updates, zone/exchange configuration maintenance, and focused repo validation workflows.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill when working on the electricitymaps-contrib repository or the
electricitymap-contrib Python package. The repo collects and standardizes
public electricity data through live parsers, installed-capacity parsers, and
static zone/exchange configuration files.
Do not use this skill for the proprietary Electricity Maps platform, frontend, API access decisions, or generic electricity-market analysis that does not touch this repository's parser/configuration workflows.
Confirm the checkout is for electricitymaps/electricitymaps-contrib and
compare it with repo provenance before
trusting stale paths, parser names, or config fields.
Install for development with uv sync --extra parsers --group dev when
possible. If a toolchain cannot use dependency groups, read
installation and checks for the pip
fallback and the difference between the parsers extra and dev tools.
Run the bundled import diagnostic before debugging path-sensitive parser imports:
python scripts/check_environment.py --repo-root <electricitymaps-contrib-checkout>
Pick the owning sub-skill below. Keep runtime work anchored in the current checkout; the references and scripts in this skill replace the need to open the original source docs or scripts by path.
| User task | Read next |
|---|---|
| Add, repair, inspect, or smoke-test a live parser for production, consumption, price, exchange, forecast, grid alerts, LMP, or intraday statistics | parsers |
Diagnose parser output shape, parser signature, timezone, missing token, optional dependency, or test-parser behavior | parsers and parser troubleshooting |
Update installed capacity values, run capacity_update, add a capacity parser, or review capacity time-series semantics | capacity |
| Understand capacity config scalar/dict/list formats, aggregate parent-zone capacity rules, or source-group updates such as EIA/EMBER/ENTSOE/IRENA/ONS/OPENNEM/REE | capacity |
Edit or validate config/zones, config/exchanges, data-center JSON, emission-factor/co2eq config, filename ordering, or geometry consistency | configuration |
| Create an aggregate zone config, validate zone/exchange filenames, or plan safe zone removal/retirement | configuration |
| Install/import failures, missing CLI entry points, missing parser extras, Node/prettier issues, or token/network boundaries shared across workflows | repo troubleshooting |
tests/test_parser_interface.py; capacity
changes start with capacity helper tests; config changes start with config
model and filename/geo tests.--list/--describe/dry-run modes until
the user has approved live calls and credentials are available.productionCapacity belongs to the capacity route, not the live-parser
smoke route. It has a different signature and mutates installed-capacity
config through update helpers.scripts/check_environment.py verifies the package, parser namespace,
config model, console entry points, and common optional imports without live
network calls.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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